propagate verbose more fully, fixes #18

simqso/lumfun.py

@@ -217,10 +217,11 @@ def logPhi(self,M,z,par=None):
                 np.log10(10**(0.4*(alpha+1)*(M-Mstar)) + \
                          10**(0.4*( beta+1)*(M-Mstar)))
     def _sample(self,Mrange,zrange,p,**kwargs):
+        zin = kwargs.pop('zin',None)
+        verbose = kwargs.pop('verbose',0)
         eps_M,eps_z = 5e-2,2e-2
         nM = int(-np.diff(Mrange(zrange)) / eps_M)
         nz = int(np.diff(zrange) / eps_z)
-        zin = kwargs.get('zin')
         if zin is None:
             # integrate across redshift to get the dN/dz distribution
             skyfrac = kwargs.get('skyArea',skyDeg2) / skyDeg2
@@ -234,7 +235,8 @@ def _sample(self,Mrange,zrange,p,**kwargs):
             Ntot = np.sum(zsamp)
             zfun = interp1d(np.cumsum(zsamp)/Ntot,zbins)
             Ntot = np.int(np.round(Ntot * skyfrac * 4*np.pi))
-            print('integration returned ',Ntot,' objects')
+            if verbose > 0:
+                print('integration returned ',Ntot,' objects')
         else:
             # redshifts supplied by user
             zfun = lambda x: zin
@@ -252,11 +254,13 @@ def _sample(self,Mrange,zrange,p,**kwargs):
             N_M = np.sum(Msamp)
             Mfun = interp1d(np.cumsum(Msamp)/N_M,Mbins)
             M[i] = Mfun(y[i])
-            if Ntot > 1e4 and ((i+1)%(Ntot//10))==0:
+            if verbose > 1 and Ntot > 1e4 and ((i+1)%(Ntot//10))==0:
                 print(i+1,' out of ',Ntot)
         return M,z
     def _fast_sample(self,Mrange,zrange,p,**kwargs):
-        print('using fast sample')
+        verbose = kwargs.pop('verbose',0)
+        if verbose > 1:
+            print('using fast sample for QLF')
         skyfrac = kwargs.get('skyArea',skyDeg2) / skyDeg2
         eps_M,eps_z = 0.05,0.10
         magLimPad = 0.2
@@ -284,10 +288,11 @@ def _fast_sample(self,Mrange,zrange,p,**kwargs):
         M,z = np.hstack(Mz)
         M += dM * (np.random.rand(len(M)) - 0.5)
         z += dz * (np.random.rand(len(M)) - 0.5)
-        print('to generate {} quasars'.format(len(M)))
+        if verbose > 1:
+            print('to generate {} quasars'.format(len(M)))
         return M,z
     def sample_from_fluxrange(self,mrange,zrange,kcorr,p=None,**kwargs):
-        fast = kwargs.get('fast_sample',False)
+        fast = kwargs.pop('fast_sample',False)
         m2M = AppToAbsMag(self.cosmo,kcorr)
         _mrange = np.array(mrange[::-1])
         _Mrange = lambda z: _mrange - m2M(_mrange,z)

simqso/sqgrids.py

@@ -1251,7 +1251,7 @@ def generateBEffEmissionLines(M1450,**kwargs):
                                            lineCatalog['name'][useLines])
     return lines
 
-def generateVdBCompositeEmLines(minEW=1.0,noFe=False):
+def generateVdBCompositeEmLines(minEW=1.0,noFe=False,verbose=0):
     tmplfits = os.path.join(datadir,'simqso_templates.fits')
     all_lines = Table(fits.getdata(tmplfits,'VdB01CompEmLines'))
     # blended lines are repeated in the table
@@ -1263,8 +1263,9 @@ def generateVdBCompositeEmLines(minEW=1.0,noFe=False):
     if noFe:
         isFe = lines['ID'].find('Fe') == 0
         lines = lines[~isFe]
-    print('using the following lines from VdB template: ', end=' ')
-    print(','.join(list(lines['ID'])))
+    if verbose > 0:
+        print('using the following lines from VdB template: ', end=' ')
+        print(','.join(list(lines['ID'])))
     c = ConstSampler
     lineList = [ [c(l['OWave']),c(l['EqWid']),c(l['Width'])] for l in lines ]
     lines = GaussianEmissionLinesTemplateVar(lineList)

simqso/sqrun.py

@@ -141,17 +141,15 @@ def buildForest(wave,z,simParams,outputDir):
     return tgrid
 
 
-def buildContinuumModels(qsoGrid,simParams):
+def buildContinuumModels(qsoGrid,simParams,verbose=0):
     continuumParams = simParams['QuasarModelParams']['ContinuumParams']
     reseed(continuumParams)
     slopes = continuumParams['PowerLawSlopes'][::2]
     breakpts = continuumParams['PowerLawSlopes'][1::2]
-    print('... building continuum grid')
+    if verbose > 0:
+        print('... building continuum grid')
     cmodel = continuumParams['ContinuumModel']
-    if cmodel in ['GaussianPLawDistribution','FixedPLawDistribution',
-                                                    'BrokenPowerLaw']:
-        if cmodel in ['GaussianPLawDistribution','FixedPLawDistribution']:
-            print('WARNING: %s continuum is deprecated' % cmodel)
+    if cmodel == 'BrokenPowerLaw':
         slopeVars = [ grids.GaussianSampler(*s) for s in slopes ]
         continuumVars = [ grids.BrokenPowerLawContinuumVar(slopeVars,
                                                            breakpts) ]
@@ -179,8 +177,9 @@ def buildEmissionLineGrid(qsoGrid,simParams):
                             emLineParams['EmissionLineModel'])
     qsoGrid.addVar(emLineGrid)
 
-def buildDustGrid(qsoGrid,simParams):
-    print('... building dust extinction grid')
+def buildDustGrid(qsoGrid,simParams,verbose=0):
+    if verbose > 0:
+        print('... building dust extinction grid')
     dustParams = simParams['QuasarModelParams']['DustExtinctionParams']
     reseed(dustParams)
     if dustParams['DustExtinctionModel'] == 'Fixed E(B-V)':
@@ -202,8 +201,8 @@ def buildDustGrid(qsoGrid,simParams):
     qsoGrid.addVar(dustVar)
 
 
-def buildFeatures(qsoGrid,wave,simParams,forest=None):
-    buildContinuumModels(qsoGrid,simParams)
+def buildFeatures(qsoGrid,wave,simParams,forest=None,verbose=0):
+    buildContinuumModels(qsoGrid,simParams,verbose=verbose)
     qsoParams = simParams['QuasarModelParams']
     if 'EmissionLineParams' in qsoParams:
         buildEmissionLineGrid(qsoGrid,simParams)
@@ -213,7 +212,7 @@ def buildFeatures(qsoGrid,wave,simParams,forest=None):
         feGrid = grids.VW01FeTemplateGrid(qsoGrid.z,wave,scales=scalings)
         qsoGrid.addVar(grids.FeTemplateVar(feGrid))
     if 'DustExtinctionParams' in qsoParams:
-        buildDustGrid(qsoGrid,simParams)
+        buildDustGrid(qsoGrid,simParams,verbose=verbose)
     if forest is not None:
         if isinstance(forest,hiforest.CachedIGMTransmissionGrid):
             losMap = forest.losMap
@@ -342,9 +341,10 @@ def buildSpectraBySightLine(wave,qsoGrid,procMap=map,
         Input wavelength grid.
     '''
     photoCache = qsoGrid.getPhotoCache(wave)
-    print('simulating ',qsoGrid.nObj,' quasar spectra')
-    print('units are ',qsoGrid.units)
-    print('max number iterations: ',maxIter)
+    if verbose > 0:
+        print('simulating ',qsoGrid.nObj,' quasar spectra')
+        print('units are ',qsoGrid.units)
+        print('max number iterations: ',maxIter)
     verby = 0 if not verbose else qsoGrid.nObj//(5*verbose)
     if qsoGrid.units == 'luminosity' or photoCache is None:
         nIter = 1
@@ -404,8 +404,9 @@ def buildSpectraBulk(wave,qsoGrid,procMap=map,
         Input wavelength grid.
     '''
     photoCache = qsoGrid.getPhotoCache(wave)
-    print('simulating ',qsoGrid.nObj,' quasar spectra')
-    print('units are ',qsoGrid.units)
+    if verbose > 0:
+        print('simulating ',qsoGrid.nObj,' quasar spectra')
+        print('units are ',qsoGrid.units)
     if qsoGrid.units == 'luminosity' or photoCache is None:
         nIter = 1
         fluxBand = None
@@ -424,7 +425,8 @@ def buildSpectraBulk(wave,qsoGrid,procMap=map,
         build_one_spec = partial(buildSpecWithPhot,wave,qsoGrid.cosmo,
                                  specFeatures,photoCache,iterNum=iterNum,
                                  saveSpectra=saveSpectra)
-        print('buildSpectra iteration ',iterNum,' out of ',nIter)
+        if verbose > 1:
+            print('buildSpectra iteration ',iterNum,' out of ',nIter)
         specOut = list(procMap(build_one_spec,qsoGrid))
         specOut = _regroup(specOut)
         synMag,synFlux,spectra = specOut
@@ -439,7 +441,8 @@ def buildSpectraBulk(wave,qsoGrid,procMap=map,
         if nIter > 1:
             # find the largest mag offset
             dm = synMag[:,fluxBand] - qsoGrid.appMag
-            print('--> delta mag mean = %.7f, rms = %.7f, |max| = %.7f' % \
+            if verbose > 1:
+                print('--> delta mag mean = %.7f, rms = %.7f, |max| = %.7f' % \
                           (dm.mean(),dm.std(),np.abs(dm).max()))
             qsoGrid.absMag[:] -= dm
             dmagMax = np.abs(dm).max()
@@ -526,19 +529,23 @@ def qsoSimulation(simParams,**kwargs):
                                                outputDir,retParams=True,
                                                clean=True)
     except IOError:
-        print(simParams['FileName']+' output not found')
+        if verbose > 0:
+            print(simParams['FileName']+' output not found')
         if 'GridFileName' in simParams:
-            print('restoring grid from ',simParams['GridFileName'])
+            if verbose > 0:
+                print('restoring grid from ',simParams['GridFileName'])
             try:
                 qsoGrid = readSimulationData(simParams['GridFileName'],
                                              outputDir)
             except IOError:
-                print(simParams['GridFileName'],' not found, generating')
+                if verbose > 0:
+                    print(simParams['GridFileName'],' not found, generating')
                 qsoGrid = buildQsoGrid(simParams)
                 qsoGrid.write(simParams,outputDir,
                               simParams['GridFileName']+'.fits')
         else:
-            print('generating QSO grid')
+            if verbose > 0:
+                print('generating QSO grid')
             qsoGrid = buildQsoGrid(simParams)
         if not forestOnly:
             if not noWriteOutput and 'GridFileName' in simParams:
@@ -585,7 +592,7 @@ def qsoSimulation(simParams,**kwargs):
     #
     # add the quasar model variables to the grid (does the random sampling)
     #
-    buildFeatures(qsoGrid,wave,simParams,forest)
+    buildFeatures(qsoGrid,wave,simParams,forest,verbose=verbose)
     timerLog('Generate Features')
     #
     # Use continuum and emission line distributions to build the components
@@ -599,7 +606,8 @@ def qsoSimulation(simParams,**kwargs):
     # map the simulated photometry to observed values with uncertainties
     #
     if not noPhotoMap:
-        print('mapping photometry')
+        if verbose > 0:
+            print('mapping photometry')
         reseed(simParams['PhotoMapParams'])
         photoData = sqphoto.calcObsPhot(qsoGrid.synFlux,qsoGrid.photoMap)
         qsoGrid.addData(photoData)